Adaptive inpainting algorithm based on DCT induced wavelet regularization

Yan Ran Li, Lixin Shen, Bruce W. Suter

Research output: Contribution to journalArticle

26 Scopus citations

Abstract

In this paper, we propose an image inpainting optimization model whose objective function is a smoothed ℓ1 norm of the weighted nondecimated discrete cosine transform (DCT) coefficients of the underlying image. By identifying the objective function of the proposed model as a sum of a differentiable term and a nondifferentiable term, we present a basic algorithm inspired by Beck and Teboulle's recent work on the model. Based on this basic algorithm, we propose an automatic way to determine the weights involved in the model and update them in each iteration. The DCT as an orthogonal transform is used in various applications. We view the rows of a DCT matrix as the filters associated with a multiresolution analysis. Nondecimated wavelet transforms with these filters are explored in order to analyze the images to be inpainted. Our numerical experiments verify that under the proposed framework, the filters from a DCT matrix demonstrate promise for the task of image inpainting.

Original languageEnglish (US)
Article number6323030
Pages (from-to)752-763
Number of pages12
JournalIEEE Transactions on Image Processing
Volume22
Issue number2
DOIs
StatePublished - Jan 21 2013

Keywords

  • Moreau envelope
  • discrete cosine transform
  • framelet
  • inpainting
  • ℓ minimization

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

Fingerprint Dive into the research topics of 'Adaptive inpainting algorithm based on DCT induced wavelet regularization'. Together they form a unique fingerprint.

  • Cite this